Abstract
Most of previous work on news relatedness focuses on news article texts. In this paper, we study the benefit of user-generated comments on modeling news relatedness. Comments contain rich text information which is provided by commenters and rated by readers with thumb-up or thumb-down, but the quality of individual comments varies widely. We compare different ways of capturing relatedness by leveraging both text and user interaction information in comments. Our evaluation based on an editorial data set demonstrates that the text information in comments is very effective to model relatedness while community rating is quite predictive of the comment quality.
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